Integration of Public Language Models and Private Services

The present invention relates to computer science, and more specifically, to artificial intelligence.

Natural language processing (NLP) refers to a branch of artificial intelligence (AI) concerned with giving computers the ability to understand text and spoken words in much the same way human beings can. In recent years, large language models (LLMs) are widely used for NLP. LLMs are a category of foundation models trained on immense amounts of data making them capable of understanding and generating natural language to perform a wide range of tasks.

According to one embodiment of the present disclosure, there is provided a computer-implemented method. The method may comprise receiving a task described at least in part with natural language. The method may further comprise instructing a public language model to split the task into a plurality of sub-tasks based on a capability of an operation pool which includes a plurality of private services, and to pair the plurality of sub-tasks with respective private services. The method may further comprise instructing the respective private services to perform the plurality of sub-tasks so as to complete the task. According to this embodiment, a symbiotic integration of the public language model and local services can be achieved.

According to some embodiments, the task may be further described with multimodal information. Further, the public language model may include a Multimodal Large Language Model (MLLM). According to these embodiments, multimodal task processing may be supported.

According to some embodiments, the capability of the operation pool may be composed of capabilities of the plurality of private services. Further, the capabilities of the plurality of private services may not be overlapped from each other. According to these embodiments, there will be no overlap on capabilities of each of the private services, such that the performance of splitting of the task by the public language model may be improved.

According to some embodiments, the method may further comprise indicating the capability of the operation pool to the public language model by describing functions of each of the plurality of private services with natural language. According to these embodiments, it may facilitate the public language model to understand the functions of the private services to perform the splitting and pairing.

According to some embodiments, the method may further comprise instructing the public language model to generate an execution order of the respective private services based on dependency relations of the private services. Further, the plurality of sub-tasks may be performed by the respective private services based on the execution order. According to these embodiments, the task may be completed in a more efficient execution flow.

According to some embodiments, the execution order may be represented by a Directed Acyclic Graph (DAG) generated by the public language model. Further, each node of the DAG may correspond to a private service with a state variable indicating an execution state of the private service. An edge between two nodes of the DAG may correspond to a dependency relation between two respective private services. According to these embodiments, the representation of the execution order may be improved.

According to some embodiments, the DAG may be generated based on a Bayesian network model. According to these embodiments, the DAG may be dynamically and efficiently generated.

According to another embodiment of the present invention, there is provided a system which may comprise one or more processors and a memory coupled to at least one of the one or more processors. The system may comprise a set of computer program instructions stored in the memory and executed by at least one of the one or more processors in order to perform an action of receiving a task described at least in part with natural language. The system may comprise a set of computer program instructions stored in the memory and executed by at least one of the one or more processors in order to perform an action of instructing a public language model to split the task into a plurality of sub-tasks based on a capability of an operation pool which includes a plurality of private services, and to pair the plurality of sub-tasks with respective private services. The system may comprise a set of computer program instructions stored in the memory and executed by at least one of the one or more processors in order to perform an action of instructing the respective private services to perform the plurality of sub-tasks so as to complete the task. According to this embodiment, a symbiotic integration of the public language model and local services can be achieved.

According to a further embodiment of the present disclosure, there is provided a computer program product. The computer program product may comprise a computer readable storage medium having program instructions embodied therewith. The program instructions may be executable by a device to perform a method. The method may comprise receiving a task described at least in part with natural language. The method may further comprise instructing a public language model to split the task into a plurality of sub-tasks based on a capability of an operation pool which includes a plurality of private services, and to pair the plurality of sub-tasks with respective private services. The method may further comprise instructing the respective private services to perform the plurality of sub-tasks so as to complete the task. According to this embodiment, a symbiotic integration of the public language model and local services can be achieved.

Various aspects of the present disclosure are described by narrative text, flowcharts, block diagrams of computer systems and/or block diagrams of the machine logic included in computer program product (CPP) embodiments. With respect to any flowcharts, depending upon the technology involved, the operations can be performed in a different order than what is shown in a given flowchart. For example, again depending upon the technology involved, two operations shown in successive flowchart blocks may be performed in reverse order, as a single integrated step, concurrently, or in a manner at least partially overlapping in time.

A computer program product embodiment (“CPP embodiment” or “CPP”) is a term used in the present disclosure to describe any set of one, or more, storage media (also called “mediums”) collectively included in a set of one, or more, storage devices that collectively include machine readable code corresponding to instructions and/or data for performing computer operations specified in a given CPP claim. A “storage device” is any tangible device that can retain and store instructions for use by a computer processor. Without limitation, the computer-readable storage medium may be an electronic storage medium, a magnetic storage medium, an optical storage medium, an electromagnetic storage medium, a semiconductor storage medium, a mechanical storage medium, or any suitable combination of the foregoing. Some known types of storage devices that include these mediums include: diskette, hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or Flash memory), static random access memory (SRAM), compact disc read-only memory (CD-ROM), digital versatile disk (DVD), memory stick, floppy disk, mechanically encoded device (such as punch cards or pits/lands formed in a major surface of a disc) or any suitable combination of the foregoing. A computer-readable storage medium, as that term is used in the present disclosure, is not to be construed as storage in the form of transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide, light pulses passing through a fiber optic cable, electrical signals communicated through a wire, and/or other transmission media. As will be understood by those of skill in the art, data is typically moved at some occasional points in time during normal operations of a storage device, such as during access, de-fragmentation or garbage collection, but this does not render the storage device as transitory because the data is not transitory while it is stored.

Computing environmentcontains an example of an environment for the execution of at least some of the computer code involved in performing the inventive methods, such as code for integration of public language models and private services. In addition to block, computing environmentincludes, for example, computer, wide area network (WAN), end user device (EUD), remote server, public cloud, and private cloud. In this embodiment, computerincludes processor set(including processing circuitryand cache), communication fabric, volatile memory, persistent storage(including operating systemand block, as identified above), peripheral device set(including user interface (UI) device set, storage, and Internet of Things (IoT) sensor set), and network module. Remote serverincludes remote database. Public cloudincludes gateway, cloud orchestration module, host physical machine set, virtual machine set, and container set.

COMPUTERmay take the form of a desktop computer, laptop computer, tablet computer, smart phone, smart watch or other wearable computer, mainframe computer, quantum computer or any other form of computer or mobile device now known or to be developed in the future that is capable of running a program, accessing a network or querying a database, such as remote database. As is well understood in the art of computer technology, and depending upon the technology, performance of a computer-implemented method may be distributed among multiple computers and/or between multiple locations. On the other hand, in this presentation of computing environment, detailed discussion is focused on a single computer, specifically computer, to keep the presentation as simple as possible. Computermay be located in a cloud, even though it is not shown in a cloud in. On the other hand, computeris not required to be in a cloud except to any extent as may be affirmatively indicated.

PROCESSOR SETincludes one, or more, computer processors of any type now known or to be developed in the future. Processing circuitrymay be distributed over multiple packages, for example, multiple, coordinated integrated circuit chips. Processing circuitrymay implement multiple processor threads and/or multiple processor cores. Cacheis memory that is located in the processor chip package(s) and is typically used for data or code that should be available for rapid access by the threads or cores running on processor set. Cache memories are typically organized into multiple levels depending upon relative proximity to the processing circuitry. Alternatively, some, or all, of the cache for the processor set may be located “off chip.” In some computing environments, processor setmay be designed for working with qubits and performing quantum computing.

Computer readable program instructions are typically loaded onto computerto cause a series of operational steps to be performed by processor setof computerand thereby effect a computer-implemented method, such that the instructions thus executed will instantiate the methods specified in flowcharts and/or narrative descriptions of computer-implemented methods included in this document (collectively referred to as “the inventive methods”). These computer readable program instructions are stored in various types of computer readable storage media, such as cacheand the other storage media discussed below. The program instructions, and associated data, are accessed by processor setto control and direct performance of the inventive methods. In computing environment, at least some of the instructions for performing the inventive methods may be stored in blockin persistent storage.

COMMUNICATION FABRICis the signal conduction paths that allows the various components of computerto communicate with each other. Typically, this fabric is made of switches and electrically conductive paths, such as the switches and electrically conductive paths that make up buses, bridges, physical input/output ports and the like. Other types of signal communication paths may be used, such as fiber optic communication paths and/or wireless communication paths.

VOLATILE MEMORYis any type of volatile memory now known or to be developed in the future. Examples include dynamic type random access memory (RAM) or static type RAM. Typically, volatile memoryis characterized by random access, but this is not required unless affirmatively indicated. In computer, the volatile memoryis located in a single package and is internal to computer, but, alternatively or additionally, the volatile memory may be distributed over multiple packages and/or located externally with respect to computer.

PERSISTENT STORAGEis any form of non-volatile storage for computers that is now known or to be developed in the future. The non-volatility of this storage means that the stored data is maintained regardless of whether power is being supplied to computerand/or directly to persistent storage. Persistent storagemay be a read only memory (ROM), but typically at least a portion of the persistent storage allows writing of data, deletion of data and re-writing of data. Some familiar forms of persistent storage include magnetic disks and solid state storage devices. Operating systemmay take several forms, such as various known proprietary operating systems or open source Portable Operating System Interface-type operating systems that employ a kernel. The code included in blocktypically includes at least some of the computer code involved in performing the inventive methods.

PERIPHERAL DEVICE SETincludes the set of peripheral devices of computer. Data communication connections between the peripheral devices and the other components of computermay be implemented in various ways, such as Bluetooth connections, Near-Field Communication (NFC) connections, connections made by cables (such as universal serial bus (USB) type cables), insertion-type connections (for example, secure digital (SD) card), connections made through local area communication networks and even connections made through wide area networks such as the internet. In various embodiments, UI device setmay include components such as a display screen, speaker, microphone, wearable devices (such as goggles and smart watches), keyboard, mouse, printer, touchpad, game controllers, and haptic devices. Storageis external storage, such as an external hard drive, or insertable storage, such as an SD card. Storagemay be persistent and/or volatile. In some embodiments, storagemay take the form of a quantum computing storage device for storing data in the form of qubits. In embodiments where computeris required to have a large amount of storage (for example, where computerlocally stores and manages a large database) then this storage may be provided by peripheral storage devices designed for storing very large amounts of data, such as a storage area network (SAN) that is shared by multiple, geographically distributed computers. IoT sensor setis made up of sensors that can be used in Internet of Things applications. For example, one sensor may be a thermometer and another sensor may be a motion detector.

NETWORK MODULEis the collection of computer software, hardware, and firmware that allows computerto communicate with other computers through WAN. Network modulemay include hardware, such as modems or Wi-Fi signal transceivers, software for packetizing and/or de-packetizing data for communication network transmission, and/or web browser software for communicating data over the internet. In some embodiments, network control functions and network forwarding functions of network moduleare performed on the same physical hardware device. In other embodiments (for example, embodiments that utilize software-defined networking (SDN)), the control functions and the forwarding functions of network moduleare performed on physically separate devices, such that the control functions manage several different network hardware devices. Computer-readable program instructions for performing the inventive methods can typically be downloaded to computerfrom an external computer or external storage device through a network adapter card or network interface included in network module.

WANis any wide area network (for example, the internet) capable of communicating computer data over non-local distances by any technology for communicating computer data, now known or to be developed in the future. In some embodiments, the WANmay be replaced and/or supplemented by local area networks (LANs) designed to communicate data between devices located in a local area, such as a Wi-Fi network. The WAN and/or LANs typically include computer hardware such as copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and edge servers.

END USER DEVICE (EUD)is any computer system that is used and controlled by an end user (for example, a customer of an enterprise that operates computer), and may take any of the forms discussed above in connection with computer. EUDtypically receives helpful and useful data from the operations of computer. For example, in a hypothetical case where computeris designed to provide a recommendation to an end user, this recommendation would typically be communicated from network moduleof computerthrough WANto EUD. In this way, EUDcan display, or otherwise present, the recommendation to an end user. In some embodiments, EUDmay be a client device, such as thin client, heavy client, mainframe computer, desktop computer and so on.

REMOTE SERVERis any computer system that serves at least some data and/or functionality to computer. Remote servermay be controlled and used by the same entity that operates computer. Remote serverrepresents the machine(s) that collect and store helpful and useful data for use by other computers, such as computer. For example, in a hypothetical case where computeris designed and programmed to provide a recommendation based on historical data, then this historical data may be provided to computerfrom remote databaseof remote server.

PUBLIC CLOUDis any computer system available for use by multiple entities that provides on-demand availability of computer system resources and/or other computer capabilities, especially data storage (cloud storage) and computing power, without direct active management by the user. Cloud computing typically leverages sharing of resources to achieve coherence and economies of scale. The direct and active management of the computing resources of public cloudis performed by the computer hardware and/or software of cloud orchestration module. The computing resources provided by public cloudare typically implemented by virtual computing environments that run on various computers making up the computers of host physical machine set, which is the universe of physical computers in and/or available to public cloud. The virtual computing environments (VCEs) typically take the form of virtual machines from virtual machine setand/or containers from container set. It is understood that these VCEs may be stored as images and may be transferred among and between the various physical machine hosts, either as images or after instantiation of the VCE. Cloud orchestration modulemanages the transfer and storage of images, deploys new instantiations of VCEs and manages active instantiations of VCE deployments. Gatewayis the collection of computer software, hardware, and firmware that allows public cloudto communicate through WAN.

Some further explanation of virtualized computing environments (VCEs) will now be provided. VCEs can be stored as “images.” A new active instance of the VCE can be instantiated from the image. Two familiar types of VCEs are virtual machines and containers. A container is a VCE that uses operating-system-level virtualization. This refers to an operating system feature in which the kernel allows the existence of multiple isolated user-space instances, called containers. These isolated user-space instances typically behave as real computers from the point of view of programs running in them. A computer program running on an ordinary operating system can utilize all resources of that computer, such as connected devices, files and folders, network shares, CPU power, and quantifiable hardware capabilities. However, programs running inside a container can only use the contents of the container and devices assigned to the container, a feature which is known as containerization.

PRIVATE CLOUDis similar to public cloud, except that the computing resources are only available for use by a single enterprise. While private cloudis depicted as being in communication with WAN, in other embodiments a private cloud may be disconnected from the internet entirely and only accessible through a local/private network. A hybrid cloud is a composition of multiple clouds of different types (for example, private, community or public cloud types), often respectively implemented by different vendors. Each of the multiple clouds remains a separate and discrete entity, but the larger hybrid cloud architecture is bound together by standardized or proprietary technology that enables orchestration, management, and/or data/application portability between the multiple constituent clouds. In this embodiment, public cloudand private cloudare both part of a larger hybrid cloud.

CLOUD COMPUTING SERVICES AND/OR MICROSERVICES (not separately shown in): private and public cloudsare programmed and configured to deliver cloud computing services and/or microservices (unless otherwise indicated, the word “microservices” shall be interpreted as inclusive of larger “services” regardless of size). Cloud services are infrastructure, platforms, or software that are typically hosted by third-party providers and made available to users through the internet. Cloud services facilitate the flow of user data from front-end clients (for example, user-side servers, tablets, desktops, laptops), through the internet, to the provider's systems, and back. In some embodiments, cloud services may be configured and orchestrated according to as “as a service” technology paradigm where something is being presented to an internal or external customer in the form of a cloud computing service. As-a-Service offerings typically provide endpoints with which various customers interface. These endpoints are typically based on a set of APIs. One category of as-a-service offering is Platform as a Service (PaaS), where a service provider provisions, instantiates, runs, and manages a modular bundle of code that customers can use to instantiate a computing platform and one or more applications, without the complexity of building and maintaining the infrastructure typically associated with these things. Another category is Software as a Service (SaaS) where software is centrally hosted and allocated on a subscription basis. SaaS is also known as on-demand software, web-based software, or web-hosted software. Four technological sub-fields involved in cloud services are: deployment, integration, on demand, and virtual private networks.

It is understood that the computing environmentinis only provided for illustration purpose without suggesting any limitation to any embodiment of this invention, for example, at least part of the program code involved in performing the inventive methods could be loaded in cache, volatile memoryor stored in other storage (e.g., storage) of the computer, or at least part of the program code involved in performing the inventive methods could be stored in other local or/and remote computing environment and be loaded when needed. For another example, the peripheral devicecould also be implemented by an independent peripheral device connected to the computerthrough interface. For a further example, the WAN may be replaced and/or supplemented by any other connection made to an external computer (for example, through the Internet using an Internet Service Provider).

With reference now to˜, some embodiments of the present disclosure will be described below.

Public language models such as LLMs may be used for understanding and generating natural language to perform a wide range of tasks. However, although LLMs have powerful capabilities for natural language processing, the inventors of the present disclosure have noticed that there are some defects regarding the usage of LLMs.

In one aspect, LLMs in the industry are typically run as a Model-as-a-Service (MaaS) on cloud servers, due to intellectual property protection and operational cost considerations. However, in industries such as banking, finance, and government, clients may not select such public models due to data privacy concerns but opt for small-scale language models running locally instead. Unfortunately, due to parameter capacity limitations, the effectiveness of small-scale language models often falls short when compared to their larger counterparts.

In another aspect, LLMs may encounter problems related to token length limitations when executing specific tasks. For example, Chat GPT-4, which is an LLM-based system developed by Open AI, supports a maximum context length for input (i.e., the token length) of 32K. Although it is relatively long compared to other LLMs, the token length is still limited. The limitation of maximum token length constrains the broader application of LLMs.

In a further aspect, LLMs trained on public data may fall prey to answering irrelevant questions when used in domain-specific knowledge Q&A. Additionally, due to the high costs associated with training and maintaining large models, it is difficult to make corrective measures at the model level once such an issue arises. Instead, developers may have to rely on prompt engineering methods to indirectly correct the errors after discovery. Unfortunately, the constant emergence of new issues has resulted in a significant workload for prompt template writing and maintenance.

On the other hand, the inventors of the present disclosure have noticed that private services/applications that run locally in enterprises have some advantages that may complement the defects of LLMs.

For example, these services can be deployed locally and privately without data security concerns. Further, they may have a wide range of uses and strong customization capabilities. In addition, since the services may be flexibly customized according to actual needs, there may be no limit on the length of the input content. Further, the services may be refined in some specific fields to obtain powerful capabilities in such fields.

However, these localized private services may still have their own defects. For example, they may have poor adaptability and migration capability. Further, they may not have the ability to handle complex tasks, due to the small scale compared to LLMs.

By weighing up the pros and cons of public language models and private services, it is proposed a solution to integrate the public language models and the private services. The public language model may act as a brain to split a proposed task into a series of sub-tasks and select corresponding private services to pair with the sub-tasks. Further, the selected private services execute the split sub-tasks respectively to complete the task. With the symbiotic integration according to the present disclosure, advantages of both public language models and private services may be brought out while defects thereof may be complemented.

The integration of public language models and private services according to some embodiments of the present disclosure may be further explained by referring to.

shows an exemplary diagram of an integration of LLM and private services according to some embodiments of the present disclosure.

As shown in, a userproposes a taskto LLM, e.g., by inputting text or spoken words of such task through an interactive interface.

LLMsplits the taskinto a plurality of sub-tasks, e.g., sub-tasks 1˜4 based on capabilityfrom an operation pool. The operation poolincludes a plurality of locally deployed private services 1˜n. For each service, it may have a single capability of perform an action to provide a corresponding service. Capabilitymay indicate the capabilities of all services included in the operation pool. By referring to capability, LLMmay understand what actions can be done by the respective services in the operation pool, and thus decide how to split the task.

Further, LLMpairs the split sub-tasks with respective services to form “sub-task and service” pairs. Accordingly, the services in the operation pool, e.g., service 1, 2, 4 and n as shown in, are chosen by LLMto perform the sub-tasks 1˜4 respectively, so as to accomplish the task.

According to the integration of LLM and private services, LLMacts as the brain to split the taskand perform task pairing with private services without accessing data of the private services, while the private services execute the sub-tasks by following the brain's thoughts. That is, LLMis only responsible for thinking not for execution, while the private services are only responsible for execution not for thinking. Accordingly, all operations that involve data and privacy may be completed by private services, which may be highly secure localized applications so as to achieve data security and privacy. Meanwhile, the ability to handle complex tasks may also be achieved since the tasks are analyzed by LLMs having powerful NLP capabilities.

The above-describedshows an exemplary integration of the LLM and private services. It is noted that, the public language model of the present disclosure may not be limited to LLM, but may be other types of public language models. Further, “public” language model in the present disclosure means that the language model is available for use by multiple entities that provides on-demand availability of natural language processing capabilities. In contrast, “private” services in the present disclosure means that the services are locally deployed for use by a single entity, such as local applications, software, etc.

Now refer to, which shows a flow chart of an exemplary method for integration of public language models and private services according to some embodiments of the present disclosure.

As shown in, in some embodiments, in Sof, one or more processors may receive a task described at least in part with natural language. The task may correspond to taskin. As an example, the task may be “initiate an approval process” input by the user.

It should be noted that the task described with natural language may not be limited to text input, but may also be spoken input or other forms, as long as it can be used to convey semantic information of the task to the processors.

Next, in S, the one or more processors may perform task splitting and pairing. Specifically, the one or more processors may instruct a public language model to split the task into a plurality of sub-tasks based on a capability of an operation pool which includes a plurality of private services, and to pair the plurality of sub-tasks with respective private service.